{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eaf245f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import load_iris\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "plt.rcParams['font.family']=['sans-serif']\n",
    "plt.rcParams['font.sans-serif']='SimHei'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ebccea7",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "98235b85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris=load_iris()\n",
    "iris.data[:20]\n",
    "# 每朵鸢尾花对应的类别\n",
    "iris.target[::20]\n",
    "# 鸢尾花的特征列的名称\n",
    "iris.feature_names\n",
    "# 鸢尾花类别的名称，即数值0,1,2分别对应的类别\n",
    "# iris.target_names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b98e2440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width  type\n",
       "0             5.1          3.5           1.4          0.2   0.0\n",
       "1             4.9          3.0           1.4          0.2   0.0\n",
       "2             4.7          3.2           1.3          0.2   0.0\n",
       "3             4.6          3.1           1.5          0.2   0.0\n",
       "4             5.0          3.6           1.4          0.2   0.0\n",
       "..            ...          ...           ...          ...   ...\n",
       "145           6.7          3.0           5.2          2.3   2.0\n",
       "146           6.3          2.5           5.0          1.9   2.0\n",
       "147           6.5          3.0           5.2          2.0   2.0\n",
       "148           6.2          3.4           5.4          2.3   2.0\n",
       "149           5.9          3.0           5.1          1.8   2.0\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.concatenate([iris.data, iris.target.reshape(-1, 1)], axis=1)\n",
    "data = pd.DataFrame(data,columns=[\"sepal_length\", \"sepal_width\", \"petal_length\",\"petal_width\", \"type\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ae8cca47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='type', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6qlt3BjBdVWdPcjbppfBUj7QTSd7N6MO5L9kWfWmh84hfkhrjEb8kNcbwS1JjDL8kNcbwS1JjDL8kNeb/AWvqd/XmJ23LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fresque=data['type'].value_counts()\n",
    "fresque\n",
    "# 计算每个类别的频率\n",
    "percentage=fresque*100/len(data)\n",
    "percentage\n",
    "sns.countplot(x='type',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "607c7a72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算花萼长度的均值\n",
    "mean=data['sepal_length'].mean()\n",
    "# 计算花萼长度的中位数\n",
    "median=data['sepal_length'].median()\n",
    "# 计算花萼长度的众数\n",
    "mode=data['sepal_length'].mode()\n",
    "# mode方法返回的是Series类型\n",
    "mode[0]\n",
    "# 也可以用scipy中的stats模块来求一组数据分众数\n",
    "from scipy import stats\n",
    "a=stats.mode(data['sepal_length']).mode\n",
    "type(mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f993e27a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x26c7faca760>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# 绘制数据的分布(直方图+密度图)\n",
    "sns.distplot(data['sepal_length'])\n",
    "plt.axvline(mean,ls='-',color='r',label='均值')\n",
    "plt.axvline(median,ls='-',color='g',label='均值')\n",
    "plt.axvline(mode[0],ls='-',color='indigo',label='众数')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "73d85328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.25, 16.5 , 20.75])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算分位数\n",
    "x=[1,3,10,15,18,20,23,40]\n",
    "np.quantile(x,q=[0.25,0.5,0.75])\n",
    "np.percentile(x,q=[25,50,75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8d52223a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.4, 5. ])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 求解鸢尾花花瓣长度的1/10和7/10的分位值\n",
    "data01=data['petal_length']\n",
    "np.quantile(data01,q=[0.1,0.7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1078dc3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.7580000000000005"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['petal_length'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ef753b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((array([4.62098120e-03, 1.12525117e-02, 1.80009726e-02, 2.47952847e-02,\n",
       "         3.16360755e-02, 3.85239852e-02, 4.54596675e-02, 5.24437896e-02,\n",
       "         5.94770330e-02, 6.65600934e-02, 7.36936817e-02, 8.08785239e-02,\n",
       "         8.81153619e-02, 9.54049537e-02, 1.02748074e-01, 1.10145515e-01,\n",
       "         1.17598086e-01, 1.25106615e-01, 1.32671950e-01, 1.40294954e-01,\n",
       "         1.47976516e-01, 1.55717541e-01, 1.63518958e-01, 1.71381715e-01,\n",
       "         1.79306786e-01, 1.87295166e-01, 1.95347874e-01, 2.03465954e-01,\n",
       "         2.11650478e-01, 2.19902542e-01, 2.28223269e-01, 2.36613811e-01,\n",
       "         2.45075352e-01, 2.53609101e-01, 2.62216302e-01, 2.70898231e-01,\n",
       "         2.79656197e-01, 2.88491542e-01, 2.97405648e-01, 3.06399929e-01,\n",
       "         3.15475843e-01, 3.24634884e-01, 3.33878589e-01, 3.43208538e-01,\n",
       "         3.52626355e-01, 3.62133712e-01, 3.71732327e-01, 3.81423968e-01,\n",
       "         3.91210458e-01, 4.01093671e-01, 4.11075537e-01, 4.21158046e-01,\n",
       "         4.31343248e-01, 4.41633258e-01, 4.52030253e-01, 4.62536483e-01,\n",
       "         4.73154266e-01, 4.83885998e-01, 4.94734150e-01, 5.05701277e-01,\n",
       "         5.16790016e-01, 5.28003096e-01, 5.39343336e-01, 5.50813654e-01,\n",
       "         5.62417068e-01, 5.74156703e-01, 5.86035796e-01, 5.98057700e-01,\n",
       "         6.10225891e-01, 6.22543972e-01, 6.35015683e-01, 6.47644904e-01,\n",
       "         6.60435665e-01, 6.73392151e-01, 6.86518714e-01, 6.99819877e-01,\n",
       "         7.13300350e-01, 7.26965031e-01, 7.40819026e-01, 7.54867654e-01,\n",
       "         7.69116461e-01, 7.83571236e-01, 7.98238019e-01, 8.13123122e-01,\n",
       "         8.28233144e-01, 8.43574986e-01, 8.59155872e-01, 8.74983370e-01,\n",
       "         8.91065411e-01, 9.07410318e-01, 9.24026827e-01, 9.40924115e-01,\n",
       "         9.58111837e-01, 9.75600151e-01, 9.93399757e-01, 1.01152194e+00,\n",
       "         1.02997861e+00, 1.04878234e+00, 1.06794644e+00, 1.08748499e+00,\n",
       "         1.10741291e+00, 1.12774605e+00, 1.14850122e+00, 1.16969631e+00,\n",
       "         1.19135038e+00, 1.21348375e+00, 1.23611811e+00, 1.25927667e+00,\n",
       "         1.28298430e+00, 1.30726765e+00, 1.33215539e+00, 1.35767837e+00,\n",
       "         1.38386988e+00, 1.41076587e+00, 1.43840530e+00, 1.46683043e+00,\n",
       "         1.49608726e+00, 1.52622591e+00, 1.55730119e+00, 1.58937321e+00,\n",
       "         1.62250803e+00, 1.65677849e+00, 1.69226523e+00, 1.72905775e+00,\n",
       "         1.76725585e+00, 1.80697118e+00, 1.84832929e+00, 1.89147197e+00,\n",
       "         1.93656019e+00, 1.98377774e+00, 2.03333576e+00, 2.08547841e+00,\n",
       "         2.14049022e+00, 2.19870541e+00, 2.26052023e+00, 2.32640936e+00,\n",
       "         2.39694803e+00, 2.47284258e+00, 2.55497368e+00, 2.64445878e+00,\n",
       "         2.74274554e+00, 2.85175617e+00, 2.97412066e+00, 3.11357350e+00,\n",
       "         3.27567439e+00, 3.46923352e+00, 3.70947380e+00, 4.02631648e+00,\n",
       "         4.49278489e+00, 5.37945782e+00]),\n",
       "  array([1. , 1.1, 1.2, 1.2, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.4, 1.4,\n",
       "         1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.5, 1.5,\n",
       "         1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.6, 1.6,\n",
       "         1.6, 1.6, 1.6, 1.6, 1.6, 1.7, 1.7, 1.7, 1.7, 1.9, 1.9, 3. , 3.3,\n",
       "         3.3, 3.5, 3.5, 3.6, 3.7, 3.8, 3.9, 3.9, 3.9, 4. , 4. , 4. , 4. ,\n",
       "         4. , 4.1, 4.1, 4.1, 4.2, 4.2, 4.2, 4.2, 4.3, 4.3, 4.4, 4.4, 4.4,\n",
       "         4.4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.6, 4.6, 4.6, 4.7,\n",
       "         4.7, 4.7, 4.7, 4.7, 4.8, 4.8, 4.8, 4.8, 4.9, 4.9, 4.9, 4.9, 4.9,\n",
       "         5. , 5. , 5. , 5. , 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.2,\n",
       "         5.2, 5.3, 5.3, 5.4, 5.4, 5.5, 5.5, 5.5, 5.6, 5.6, 5.6, 5.6, 5.6,\n",
       "         5.6, 5.7, 5.7, 5.7, 5.8, 5.8, 5.8, 5.9, 5.9, 6. , 6. , 6.1, 6.1,\n",
       "         6.1, 6.3, 6.4, 6.6, 6.7, 6.7, 6.9])),\n",
       " (1.5113354549453473, 2.258770622308308, 0.8303170826271503))"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x=data\n",
    "x=data['petal_length']\n",
    "# 对数正态分布\n",
    "stats.probplot(x,dist=stats.logistic,plot=plt)\n",
    "# 正态分布\n",
    "stats.probplot(x,dist=stats.norm,plot=plt)\n",
    "# 指数分布\n",
    "stats.probplot(x,dist=stats.expon,plot=plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e8da9082",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    2\n",
      "1    7\n",
      "dtype: int64\n",
      "[2]\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "s=[7,4,2,6,5,7,8,2,2,2,7,7]\n",
    "data=pd.Series(s)\n",
    "print(data.mode())\n",
    "print(stats.mode(data).mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "277c7c55",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\python-3.9.2\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出一个对数正态分布\n",
    "# 方法一\n",
    "# 首先取出一组呈现正态分布的数据，0代表均值，1代表方差\n",
    "data=np.random.normal(0,1,10000)\n",
    "da=np.exp(data)\n",
    "sns.distplot(da)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0a38fd1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\python-3.9.2\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 方法二\n",
    "dase=np.random.lognormal(0,1,10000)\n",
    "sns.distplot(dase)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
